The concept of a large margin is central to support vector machines and it has recently been adapted and applied for nearest neighbour classification. In this paper, a modification of this method is proposed in order ...
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A cloud-distributed optimization algorithm applicable to large scale, constrained, multiobjective, optimization problems, such as steamflood redistribution, is presented. The proposed algorithm utilizes the so-called ...
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ISBN:
(纸本)9781510841970
A cloud-distributed optimization algorithm applicable to large scale, constrained, multiobjective, optimization problems, such as steamflood redistribution, is presented. The proposed algorithm utilizes the so-called Metamodel Assisted evolutionary Algorithm (MAEA) as its algorithmic basis. MAEAs use a generic implementation of an evolutionary algorithm as their main optimization engine and advanced machine learning techniques as metamodels. Metamodels are utilized through the application of an inexact pre-evaluation phase during the optimization, which substantially decreases the evaluations of the problem specific forward model. Additionally, a unification of global search (GS) and local search (LS) is achieved via the use of Lamarckian learning principles applied on top of a MAEA creating, in essence, a Metamodel Assisted Memetic Algorithm (MAMA). MAMAs profit from the abilities of MAEAs to explore the most promising regions of the design space without being trapped in local optima while also utilizing the efficiency of deterministic methods to further refine promising solutions located during GS. At the end of each EA generation, the most promising members of the current populations are selected to undergo LS using a gradient-based method. Further, integration with scalable cloud-distributed computing allows MAMAs (CD-MAMA) to perform rapid and simultaneous evaluation of tens of thousands of operating plans. The proposed algorithm has been statistically validated on two mathematical test cases and, subsequently, used to optimize a field undergoing steamflood under two different oil-price scenarios. Thus, demonstrating that, cloud-distributed MAMAs coupled with efficient reservoir models, allow for steamflood injection redistribution optimization in affordable, by industry, wallclock times (hours). For the field in question production comes from poorly consolidated sands within the Antelope Shale member of the Miocene Monterey formation with porosity averaging 30
MOEA/D is a novel multiobjective evolutionary algorithm based on decomposition approach, which has attracted much attention in recent years. However, when tackling the problems with irregular (e.g., disconnected or de...
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The problems related with urban fires brings destruction, casualties and economic issues to the country. On aiming to decrease the risk specially on populous areas, the development of classification and/or forecasting...
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System simulation without detailed prior knowledge or data of the system is a complex challenge. In this paper we present an approach to automatically generate a model on the fly in a symbiotic way. Basically the data...
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This paper proposes some new evolutionary and classification methods for the delineation of local labor markets (LLMs) in areas where there are a large number of small localities with little labor interaction. The evo...
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This paper proposes some new evolutionary and classification methods for the delineation of local labor markets (LLMs) in areas where there are a large number of small localities with little labor interaction. The evolutionary methods presented here, based on previous works of Flrez-Revuelta et al. (Int J Autom Comput 5:10-21, 2008a;PPSN X, LNCS 5199:1011-1020, 2008b) and Martinez-Bernabeu et al. (Expert Syst Appl 39:6754-6766, 2012), decrease their computational times (up to a 99 %) without deteriorating the quality and robustness of the solutions. Also, in this work we avoid geographical contiguity constraints because such restrictions might reduce the realism of the process. Another contribution of this paper is related to the location of new services-hospitals, schools, employment centers, etc.-taking into account the labor mobility patterns. In this context, we present a cluster partitioning of k-means procedure, which captures the common aspects of all the potential solutions of these evolutionary algorithms and allows us to rank the LLMs foci, understood as the main centers of activity of the markets. The performance of the algorithms is analyzed through a real commuting dataset of the region of Aragn (Spain).
The main objective of this research was to compare the results obtained from modelling irrigation water allocation decisions within a single-stage decision-making framework with the results obtained within a multi-sta...
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The main objective of this research was to compare the results obtained from modelling irrigation water allocation decisions within a single-stage decision-making framework with the results obtained within a multi-stage sequential decision-making framework under a full water quota and a restricted water quota. A unified irrigation decision-making framework was developed to model the impact of the interaction between water availability, irrigation area and irrigation scheduling decisions as multi-stage sequential decisions on gross margin variability. An Excel ® risk simulation model that utilises evolutionary algorithms embedded in Excel® based on the Soil Water Irrigation Planning and Energy management (SWIP-E) programming model was developed and applied to optimise irrigation water use. The model facilitates the simulation of the economic consequences resulting from changes to the key decision variables that need to be optimised through gross margin calculations for each state of nature. Risk enters the simulation model as crop yield risk through different potential crop yields in each state of nature and stochastic weather which determines irrigation management decisions. Water budget calculations were replicated to include 12 states of nature within a crop rotation system of maize and wheat. The risk simulation model was applied in Douglas, a typical location of an irrigation farm. The results showed improved risk management within a multi-stage decision-making framework as indicated by higher gross margins and reduced variability due to improved irrigation scheduling decisions under both a full and restricted water quota scenario. Close to potential yields, if not full potential yields were achieved within both decision-making frameworks. However, a significant reduction in per state irrigation water use resulted within a multi-stage decision-making framework sequentially resulting in improved gross margins. A full irrigation strategy with reduced areas was fol
In this paper a co-processor for the hardware aided decision tree induction using evolutionary approach (EFTIP) is proposed. EFTIP is used for hardware acceleration of the fitness evaluation task since this task is pr...
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In this paper a co-processor for the hardware aided decision tree induction using evolutionary approach (EFTIP) is proposed. EFTIP is used for hardware acceleration of the fitness evaluation task since this task is proven in the paper to be the execution time bottleneck. The EFTIP co-processor can significantly improve the execution time of a novel algorithm for the full decision tree induction using evolutionary approach (EFTI) when used to accelerate the fitness evaluation task. The comparison of the HW/SW EFTI implementation with the pure software implementation suggests that the proposed HW/SW architecture offers substantial DT induction time speedups for the selected benchmark datasets from the standard UCI machine learning repository database. (C) 2016 Elsevier B.V. All rights reserved.
In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approa...
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In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.
In this paper, we propose a complete, fully automatic and efficient clinical decision support system for breast cancer malignancy grading. The estimation of the level of a cancer malignancy is important to assess the ...
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In this paper, we propose a complete, fully automatic and efficient clinical decision support system for breast cancer malignancy grading. The estimation of the level of a cancer malignancy is important to assess the degree of its progress and to elaborate a personalized therapy. Our system makes use of both Image Processing and Machine Learning techniques to perform the analysis of biopsy slides. Three different image segmentation methods (fuzzy c-means color segmentation, level set active contours technique and grey-level quantization method) are considered to extract the features used by the proposed classification system. In this classification problem, the highest malignancy grade is the most important to be detected early even though it occurs in the lowest number of cases, and hence the malignancy grading is an imbalanced classification problem. In order to overcome this difficulty, we propose the usage of an efficient ensemble classifier named EUSBoost, which combines a boosting scheme with evolutionary undersampling for producing balanced training sets for each one of the base classifiers in the final ensemble. The usage of the evolutionary approach allows us to select the most significant samples for the classifier learning step (in terms of accuracy and a new diversity term included in the fitness function), thus alleviating the problems produced by the imbalanced scenario in a guided and effective way. Experiments, carried on a large dataset collected by the authors, confirm the high efficiency of the proposed system, shows that level set active contours technique leads to an extraction of features with the highest discriminative power, and prove that EUSBoost is able to outperform state-of-the-art ensemble classifiers in a real-life imbalanced medical problem. (C) 2015 Elsevier B.V. All rights reserved.
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